class_docstr: Write me
      
code: def compile(self, optimizer, metrics=[]):
      metrics += [mean_q]  # register default metrics
  
      def clipped_masked_error(args):
          y_true, y_pred, mask = args
          loss = huber_loss(y_true, y_pred, self.delta_clip)
          loss *= mask  # apply element-wise mask
          return K.sum(loss, axis=-1)
  
      # Create trainable model. The problem is that we need to mask the output since we only
      # ever want to update the Q values for a certain action. The way we achieve this is by
      # using a custom Lambda layer that computes the loss. This gives us the necessary flexibility
      # to mask out certain parameters by passing in multiple inputs to the Lambda layer.
      y_pred = self.model.output
      y_true = Input(name="y_true", shape=(self.nb_actions,))
      mask = Input(name="mask", shape=(self.nb_actions,))
      loss_out = Lambda(
          clipped_masked_error, output_shape=(1,), name="loss"
      )([y_pred, y_true, mask])
      ins = (
          [self.model.input]
          if type(self.model.input) is not list
          else self.model.input
      )
      trainable_model = Model(
          inputs=ins + [y_true, mask], outputs=[loss_out, y_pred]
      )
      assert len(trainable_model.output_names) == 2
      combined_metrics = {trainable_model.output_names[1]: metrics}
      losses = [
          lambda y_true, y_pred: y_pred,  # loss is computed in Lambda layer
          lambda y_true, y_pred: K.zeros_like(
              y_pred
          ),  # we only include this for the metrics
      ]
      trainable_model.compile(
          optimizer=optimizer, loss=losses, metrics=combined_metrics
      )
      self.trainable_model = trainable_model
  
      self.compiled = True
  
imports: import collections
  import numpy
  from keras.callbacks import History
  from keras.models import Model
  from keras.layers import Input, Lambda
  import keras.backend
  from rl.core import Agent
  from rl.agents.dqn import mean_q
  from rl.util import huber_loss
  from rl.policy import EpsGreedyQPolicy, GreedyQPolicy
  from rl.util import get_object_config
label_desc: Use image node_id 6 for calling the SARSAAgent obj's underlying member method code with example usage: obj.compile(optimizer, metrics) and returns: K